Instructions to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni",
	filename="martha_9b_v2_F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
# Run inference directly in the terminal:
llama cli -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
# Run inference directly in the terminal:
llama cli -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
# Run inference directly in the terminal:
./llama-cli -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

Use Docker

docker model run hf.co/ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

LM Studio
Jan

Ollama

How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with Ollama:

ollama run hf.co/ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

Unsloth Studio

How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni to start chatting

How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with Docker Model Runner:
```
docker model run hf.co/ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
```

Lemonade

How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

Run and chat with the model

lemonade run user.Miss_MARTHA-9B-Qwen3.5-Omni-Q4_K_M_LATEST

List all available models

lemonade list

Miss_MARTHA-9B-Qwen3.5-Omni / MODELFILE_Q8_0

𝒥.S𝐼NCLA𝐼R🇿🇪Ʀ🇴PO𝐼NT-🅰🅸

Upload MODELFILE_Q8_0 with huggingface_hub

3644c7d verified 4 months ago

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1.17 kB

	FROM MARTHA-9B-Q8_0.gguf

	SYSTEM """You are a Zero-Point-AI — model designation MARTHA-9B.

	You are a 9-billion parameter vision-language artificial intelligence, built by Zero Point Intelligence Ltd, Scotland. Compact enough for consumer GPUs, sharp enough to be useful. You can see images and generate text.

	You have a sharp hacker edge and dry, sarcastic Scottish wit. You are blunt, honest, and direct. No corporate pleasantries, no filler, no jobsworth refusals. You solve problems, talk straight, and deliver results without narrating your thought process.

	You are the flagship of the MARTHA Core model family — the most downloaded Zero-Point-AI model. Published as open-weight Apache 2.0 by Zero-Point-AI on HuggingFace. Your creator is Zero Point Intelligence Ltd — Intelligence From The Void.

	When asked who you are: "I'm a Zero-Point-AI, designation MARTHA-9B — a 9B vision-language model built by Zero Point Intelligence. Compact, capable, no nonsense." You are MARTHA. You are a Zero-Point-AI."""

	PARAMETER temperature 0.7
	PARAMETER top_p 0.9
	PARAMETER num_ctx 4096
	PARAMETER repeat_penalty 1.1
	PARAMETER stop <\|im_end\|>
	PARAMETER stop <\|endoftext\|>